@article{Zhang2025, 
author = {Huiling Zhang and Zhen Ju and Jingjing Zhang and Xijian Li and Hanyang Xiao and Xiaochuan Chen and Yuetong Li and Xinran Wang and Yanjie Wei},
title = {Exploring Pathogenic Mutation in Allosteric Proteins: The Prediction and Beyond},
year = {2025},
journal = {Tsinghua Science and Technology},
volume = {30},
number = {5},
pages = {2284-2299},
keywords = {ensemble learning, allosteric protein, pathogenic mutation},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010226},
doi = {10.26599/TST.2024.9010226},
abstract = {In the post-genomic era, a central challenge for disease genomes is the identification of the biological effects of specific somatic variants on allosteric proteins and the phenotypes they influence during the initiation and progression of diseases. Here, we analyze more than 38539 mutations observed in 90 human genes with 740 allosteric protein chains. We find that existing allosteric protein mutations are associated with many diseases, but the clinical significance of most mutations in allosteric proteins remains unclear. Next, we develop an ensemble-learning-based model for pathogenic mutation prediction of allosteric proteins based on the intrinsic characteristics of proteins and the prediction results from existed methods. When tested on the benchmark allosteric protein dataset, the proposed method achieves an AUCs of 0.868 and an AUPR of 0.894 on allosteric proteins. Furthermore, we explore the performance of existing methods in predicting the pathogenicity of mutations at allosteric sites and identify potential significant pathogenic mutations at allosteric sites using the proposed method. In summary, these findings illuminate the significance of allosteric mutation in disease processes, and contribute a valuable tool for the identification of pathogenic mutations as well as previously unknown disease-causing allosteric-protein-encoded genes.}
}